2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

BACKGROUND: Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. METHODS: Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. RESULTS: 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. CONCLUSIONS: It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.

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Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.

The widespread presence of motion sensors on users' personal mobile devices has spawned a growing research interest in human activity recognition (HAR). However, when deployed at a large-scale, e.g., on multiple devices, the performance of a HAR system is often significantly lower than in reported research results. This is due to variations in training and test device hardware and their operating system characteristics among others. In this paper, we systematically investigate sensor-, device- and workload-specific heterogeneities using 36 smartphones and smartwatches, consisting of 13 different device models from four manufacturers. Furthermore, we conduct experiments with nine users and investigate popular feature representation and classification techniques in HAR research. Our results indicate that on-device sensor and sensor handling heterogeneities impair HAR performances significantly. Moreover, the impairments vary significantly across devices and depends on the type of recognition technique used. We systematically evaluate the effect of mobile sensing heterogeneities on HAR and propose a novel clustering-based mitigation technique suitable for large-scale deployment of HAR, where heterogeneity of devices and their usage scenarios are intrinsic.

Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition

Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, we show that DeepSense is feasible to implement on smartphones and embedded devices thanks to its moderate energy consumption and low latency.

https://dl.acm.org/doi/pdf/10.1145/3038912.3052577

DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

List of Acronyms and Abbreviations List of Figures, Tables and Boxes Preface About the Contributors Part 1: Introduction 1. The Ecological Challenge in Organization Theory and Organizational Behaviour Ralph Meirna and Richard Welford Part 2: Framing the Organizational Dimensions of the Natural Environment 2. Defining the Problem: Diagnostic 'Tools' to Explore the Evolution of Unsustainable Practices in Organizations Tony Ernerson and Richard Welford 3. The Challenge of Ecological Logic: Explaining Distinctive Organizational Phenomena in Corporate Environmental Management Ralph Meima 4. Power, Organizational Culture and Ecological Abuse Tony Ernerson and Richard Welford Part 3: Developing Strategies for Managerial Action 5. Developing an Environmental Culture Through Organizational Change and Learning Minna Halrne 6. Reassessing Culture and Strategy: Environmental Improvement, Structure, Leadership and Control John Dodge 7. Culture Change, Pluralism and Participation David Jones and Richard Welford 8. Human Resource Management, Strategic Organizational Capabilities and Sustainable Development Tony Ernerson, Rdph Meimu, Romney Tansley and Richard Welford References Index

Culture and organizations

The majority of activity recognition systems in wearable computing rely on a set of statistical measures, such as means and moments, extracted from short frames of continuous sensor measurements to perform recognition. These features implicitly quantify the distribution of data observed in each frame. However, feature selection remains challenging and labour intensive, rendering a more generic method to quantify distributions in accelerometer data much desired. In this paper we present the ECDF representation, a novel approach to preserve characteristics of arbitrary distributions for feature extraction, which is particularly suitable for embedded applications. In extensive experiments on six publicly available datasets we demonstrate that it outperforms common approaches to feature extraction across a wide variety of tasks.

On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution

Digital maps such as Google Maps, Yelp, and Waze represent an incredible HCI success-they have transformed the way people navigate and access information about the world. However, there is a twofold problem limiting who can use these systems and how they benefit. First, these platforms focus almost exclusively on data about road networks and points of interest (POIs), noticeably lacking information about pedestrian infrastructure and physical accessibility. Second, because of their graphical nature and reliance on gesture and mouse input, digital maps can be inaccessible to some users-for example, those with visual or upper-body motor impairments. Thus, at a high level, there are two key accessibility problems related to accessible maps: 1) How can we collect, validate, and integrate accessibility information about the physical world into maps? 2) How can we design digital maps to be accessible to a diverse set of users across a wide range of physical, sensory, and cognitive abilities? Active research in HCI and beyond exists in both areas, but there has been no direct effort to unite this research community. To begin addressing this gap, we recently organized a Special Interest Group (SIG) at CHI2018 entitled "Making Maps Accessible and Putting Accessibility in Maps" (Figure 1). We set forth three explicit goals: First, to bring together and network scholars and practitioners who are broadly interested in accessible maps; second, to identify grand challenges and future research trajectories; and third, to establish accessible maps as a valuable topic within HCI. Accessibility is a broad, multifaceted topic. We assembled co-organizers from both academia and industry with varying topical expertise and regional and cultural experiences. The SIG attracted roughly 25 participants, including three telepresence robots, and interwove small-group brainstorming and discussion with large-group summary presentations. The two primary discussion topics were identifying key challenges and seeding potential solutions in the area of accessible maps. Below, we synthesize key themes and enumerate rich, open paths for future work, which emerged from the SIG (Table 1)

https://hal-enac.archives-ouvertes.fr/hal-02048705/document

Grand challenges in accessible maps

People with high-functioning autism face challenges in communication and social interaction. This article considers the possibility, and perhaps inevitability, of wearable devices such as Google Glass being used as real-time assistive technologies for this group, with the intent of enabling them to better access our complex social world. Social impairments, by their very nature, highlight issues of communication, personal information, and social judgment. In considering such assistive technology in this context, the authors explore new tensions between privacy issues and assistive technologies, especially those of a do-it-yourself nature, which are not immediately solvable within our current privacy frameworks. This article is part of a special issue on privacy and security.

Social Access vs. Privacy in Wearable Computing: A Case Study of Autism

The Break-Time Barometer is a social awareness system, which was developed as part of an exploratory study of the use of situated sensing and displays to promote cohesion in a newly-dispersed workplace. The Break-Time Barometer specifically aims to use an ambient persuasion approach in order to encourage people to join existing breaks, which take place within this community. Drawing upon a privacy-sensitive ubiquitous sensing infrastructure, the system of-fers information about potentially break-related activity in social spaces within this workplace, including alerts when specific events are detected. The system was developed using a user-centered iterative design approach. A qualitative mixed methods evaluation of a full deployment identified a diverse set of reactions to both the system and the design goal, and further elaborated the challenges of designing for social connectedness in this complex workplace context.

The break-time barometer: an exploratory system forworkplace break-time social awareness

Addressing human values in design has become an increasingly important consideration in the design of interactive systems. Within HCI, this trend is perhaps best emphasized by the increased volume of work that follows a Value Sensitive Design (VSD) approach. This trend is mirrored in human rights, especially in Europe, where individual values have been increasingly incorporated into the jurisprudence on the European Convention on Human Rights (ECHR). There are a number of striking similarities between VSD and recent ECHR jurisprudence. This paper explores those similarities and how ECHR jurisprudence may be used to help with Values in Design problems and vice versa, thereby enabling VSD and similar approaches to be considered from a human rights perspective, whilst contributing to debates about the future of Values in Design.

Reuben Kirkham

Papers

On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution

Grand challenges in accessible maps

Social Access vs. Privacy in Wearable Computing: A Case Study of Autism

The break-time barometer: an exploratory system forworkplace break-time social awareness

Using European Human Rights Jurisprudence for Incorporating Values into Design